A Semantic Knowledge based Context-aware Formalism for Smart Border Surveillance System

This paper presents a semantic knowledge-based context-aware smart border surveillance system to monitor suspicious activities across the border or nearby red zones and assists in handling hazardous situations using context acquisition, observation, monitoring, and generating alerts without or with minimal human intervention. The proposed formalism has an intelligent decision support mechanism to take decisions intelligently on behalf of the user, infer a plan to prevent the damages caused by the suspicious entities, and adapt itself according to the current context of use. We propose algorithms for a smart border vigilance system to present the system flow on detecting suspicious activities in the red zone and rescuing from hazards. We develop a simple case study of a smart border surveillance system and simulate the agents’ reasoning process using NetLogo simulator to analyze the behaviour in terms of efficiency and efficacy of the system.